Objective: This study aims to evaluate the functional significance of coronary stenosis by analyzing low-level radiomic features of the pericoronary adipose tissue (PCAT) surrounding the lesions, which are indicative of its inflammation status. Methods: A dataset of 72 patients who underwent coronary computed tomography angiography (CCTA) was analyzed, with 3D segmentation and computational fluid dynamics (CFD) simulations from a prior study. Centerlines of the main epicardial branches were automatically extracted, and lesions identified using Gaussian kernel regression to estimate healthy branch caliber. PCAT features were computed per vessel following guideline recommendations and per lesion within a region extending radially for two vessel radii. Features like fat volume and mean attenuation (FAI) were analyzed for their relationship with CFD-derived hemodynamic biomarkers, such as fractional flow reserve (FFR) and wall shear stress (WSS). These features also informed a machine learning (ML) model for classifying potentially ischemic lesions. Results: PCAT exhibited, on average, higher attenuation in the presence of hemodynamically significant lesions (i.e., FFR < 0.80), although this difference was of limited statistical significance. The ML classifier, trained on PCAT features, successfully distinguished potentially ischemic lesions, yielding average accuracy of 0.84. Conclusion: PCAT attenuation is correlated with the functional status of coronary stenosis and can be used to inform ML models for predicting potential ischemia. Significance: PCAT features, readily available from CCTA, can be used to predict the hemodynamic characteristics of a lesion without the need for an invasive FFR examination.

OBJECTIVES:To assess whether ascending aorta over-angulation, a morphological feature recently found to be associated with acute type A aortic dissection, precedes dissection and how it affects wall stress distribution. METHODS:A baseline finite element model, previously created by a neural network tool from end-diastolic computed tomography angiography measurements in 124 healthy subjects, was modified to simulate the over-angulation accompanying aortic elongation, obtaining paradigmatic models with different ascending angulations (ascending-arch angle 145°-110°). The models were discretized and embedded in a deformable continuum representing surrounding tissues, aortic wall anisotropy and nonlinearity were accounted for, pre-tensioning at diastolic pressures was applied and peak systolic stresses were computed. Then, from 15 patients' pre-dissection geometries, patient-specific finite element models of pre-dissection aorta were created through the same framework. The sites of maximum longitudinal stress were compared with the respective sites of dissection entry tear in post-dissection imaging. RESULTS:Paradigmatic models showed that progressive narrowing of the ascending-arch angle was associated with increasing longitudinal stress (becoming significant for angles <130°), whereas the impact on circumferential stress was less consistent. In pre-dissection patient-specific models, the ascending-arch angle was narrowed (113°±11°), and the region of peak longitudinal stresses corresponded to the entry tear location in the respective post-dissection computed tomography angiography. CONCLUSIONS:This study strongly supports the hypothesis that the ascending-arch angle, as quantifier of aorta over-angulation, can be a good predictor of aortic dissection, since its narrowing below 130° increases longitudinal wall stress, and the dissection entry tears develop in the aortic wall in areas of highest longitudinal stress.

INTRODUCTION:Dacron graft replacement is the standard therapy for ascending aorta aneurysm, involving the insertion of a prosthesis with lower compliance than native tissue, which can alter downstream hemodynamics and lead to adverse remodeling. Digital human twins (DHT), based on in-silico models, have the potential to predict biomarkers of adverse outcome and aid in designing optimal treatments tailored to the individual patient. OBJECTIVE:We propose a pipeline for deploying a digital human twin of the thoracic aorta to explore alternative solutions to traditional Dacron grafting, utilizing more compliant prostheses for reconstructing the ascending aorta. METHODS:We propose a DHT based on fluid-structure interaction (FSI) analysis of the thoracic aorta. We create 3 models of the patient, representing: (i) the pre-operative baseline, (ii) the post-operative with Dacron graft, and (iii) a virtual post-operative using a compliant fibrous prosthesis. 3D geometry of the thoracic aorta for a patient with a congenital aneurysm, before and after the surgery, were reconstructed from magnetic resonance imaging (MRI). As inlet boundary condition (BC), we assigned a time-varying 3D velocity profile extrapolated from 4D flow MRI. For the outlet BCs, we coupled 0D Windkessel models, tuned to match the flow rate measured in the descending aorta from 4D flow. The aortic wall and the prosthetic graft were modeled as hyperelastic materials using the Holzapfel-Gasser constitutive model and tuned to patients distensibility. FSI analysis was run for two cardiac cycles. RESULTS:Results were validated against 4D flow data. Quantitative comparison of outflows between FSI and 4D flow yielded relative squared errors of 5.28% and 0.33% for models (i) and (ii), respectively. Wall shear stress (WSS) and strain increased in both post-surgical scenarios (ii) and (iii) compared to (i), with a lower increase observed in the virtual scenario (iii) (p<0.001). However, the difference between scenarios (iii) and (ii) remained moderate on average (e.g., 0.6 Pa for WSS). CONCLUSION:FSI analysis enables the deployment of reliable thoracic aorta DHTs to predict the impact of prostheses with different distensibility. Results indicate moderate yet promising benefits of more compliant fibrous devices on distal hemodynamics.

Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used. The Teacher model, an ensemble of three convolutional neural networks, is trained on end-systole and end-diastole frames and is used to generate MV pseudo-segmentations on intermediate frames of the cardiac cycle. The pseudo-annotated frames augment the Student model's training set, improving segmentation accuracy and temporal consistency. The Student outperforms individual Teachers, achieving a Dice score of 0.82, an average surface distance of 0.37 mm, and a 95% Hausdorff distance of 1.72 mm for MV leaflets. The Student model demonstrates reliable frame-by-frame MV segmentation, accurately capturing leaflet morphology and dynamics throughout the cardiac cycle, with a significant reduction in inference time compared to the ensemble. This approach greatly reduces manual annotation workload and ensures reliable, repeatable, and time-efficient MV analysis. Our method holds strong potential to enhance the precision and efficiency of MV diagnostics and treatment planning in clinical settings.

Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. This may lead to topological inconsistencies and suboptimal performance in low-data regimes. To address these challenges, we propose a data-efficient deep learning method for direct 3D anatomical object surface meshing using geometric priors. Our approach employs a multi-resolution graph neural network that operates on a prior geometric template which is deformed to fit object boundaries of interest. We show how different templates may be used for the different surface meshing targets, and introduce a novel masked autoencoder pretraining strategy for 3D spherical data. The proposed method outperforms nnUNet in a one-shot setting for segmentation of the pericardium, left ventricle (LV) cavity and the LV myocardium. Similarly, the method outperforms other lumen segmentation operating on multi-planar reformatted images. Results further indicate that mesh quality is on par with or improves upon marching cubes post-processing of voxel mask predictions, while remaining flexible in the choice of mesh triangulation prior, thus paving the way for more accurate and topologically consistent 3D medical object surface meshing.

Coronary artery disease involves the narrowing of coronary vessels due to atherosclerosis and is currently the leading cause of death worldwide. The gold standard for its diagnosis is the fractional flow reserve (FFR) examination, which measures the trans-stenotic pressure ratio during maximal vasodilation. However, the invasiveness and cost of this procedure have prompted the development of computer-based virtual FFR (vFFR) computation, which simulates coronary flow using computational fluid dynamics (CFD) techniques. Geometric deep learning algorithms have recently shown the capability to learn features on meshes, including applications in cardiovascular research. In this work, we aim to conduct a comprehensive empirical analysis of different backends for predicting vFFR fields in coronary arteries, serving as surrogates for CFD simulations. We evaluate six different backends and compare their performance in learning hemodynamics on meshes using CFD solutions as ground truth. This study is divided into two main parts: i) First, we use a dataset of 1,500 synthetic bifurcations of the left coronary artery. Each model is trained to predict various pressure-related fields, from which the vFFR field is reconstructed. We compare the models' performance when different learning variables are used during training. ii) Second, we use a dataset of 427 patient-specific CFD simulations from a previous study by our group. Here, we repeat the experiments conducted on the synthetic dataset, focusing on the learning variable that yielded the best performance in the synthetic dataset. Most backends achieved very good performance on the synthetic dataset, particularly when learning the pressure drop over the manifold. For other network output variables (e.g., pressure and the vFFR field), transformer-based backends outperformed all other architectures. When trained on patient-specific data, transformer-based architectures were the only ones to achieve strong performance, both in terms of average per-point error and in accurately predicting vFFR in stenotic lesions. Our findings indicate that various geometric deep learning backends can serve as effective CFD surrogates for problems involving simple geometries. However, for tasks involving datasets with complex and heterogeneous topologies, transformer-based networks are the optimal choice. Additionally, pressure drop emerged as the optimal network output for learning pressure-related fields.

BACKGROUND AND OBJECTIVE:The MitraClip is the most widely used percutaneous treatment for mitral regurgitation, typically performed under the real-time guidance of 3D transesophageal echocardiography (TEE). However, artifacts and low image contrast in echocardiography hinder accurate clip visualization. This study presents a proof-of-concept of an automated pipeline for clip detection from 3D TEE images acquired in a controlled in vitro simulation environment. METHODS:An Attention UNet was employed to segment the device, while a DenseNet classifier predicted its configuration among ten possible states, ranging from fully closed to fully open. Based on the predicted configuration, a template model derived from computer-aided design (CAD) was automatically registered to refine the segmentation and enable quantitative characterization of the device. The pipeline was trained and validated on 196 3D TEE images acquired using a heart simulator, with ground-truth annotations refined through CAD-based templates. RESULTS:The Attention UNet achieved an average surface distance of 0.76 mm and a 95% Hausdorff distance of 2.44 mm for segmentation, while the DenseNet achieved an average weighted F1-score of 0.80 for classification. Post-refinement, segmentation accuracy improved, with average surface distance and 95% Hausdorff distance reduced to 0.69 mm and 1.83 mm, respectively. CONCLUSION:This pipeline enhanced clip visualization, providing fast and accurate detection with quantitative feedback, potentially improving procedural efficiency and reducing adverse outcomes.

AbstractIn transcatheter aortic valve implantation (TAVI), final device position may be affected by device interaction with the whole aortic landing zone (LZ) extending to ascending aorta. We investigated the impact of aortic LZ curvature and angulation on TAVI implantation depth, comparing short-frame balloon-expanding (BE) and long-frame self-expanding (SE) devices. Patients (n = 202) treated with BE or SE devices were matched based on one-to-one propensity score. Primary endpoint was the mismatch between the intended (HPre) and the final (HPost) implantation depth. LZ curvature and angulation were calculated based on the aortic centerline trajectory available from pre-TAVI computed tomography. Total LZ curvature ($${k}_{LZ,tot}$$ k L Z , t o t ) and LZ angulation distal to aortic annulus ($${\alpha }_{LZ,Distal}$$ α L Z , D i s t a l ) were greater in the SE compared to the BE group (P < 0.001 for both). In the BE group, HPost was significantly higher than HPre at both cusps (P < 0.001). In the SE group, HPost was significantly deeper than HPre only at the left coronary cusp (P = 0.013). At multivariate analysis, $${\alpha }_{LZ,Distal}$$ α L Z , D i s t a l was the only independent predictor (OR = 1.11, P = 0.002) of deeper final implantation depth with a cut-off value of 17.8°. Aortic LZ curvature and angulation significantly affected final TAVI implantation depth, especially in high stent-frame SE devices reporting, upon complete release, deeper implantation depth with respect to the intended one.

Diastolic vortex ring (VR) plays a key role in the blood-pumping function exerted by the left ventricle (LV), with altered VR structures being associated with LV dysfunction. Herein, we sought to characterize the VR diastolic alterations in ischemic cardiomyopathy (ICM) patients with systo-diastolic LV dysfunction, as compared to healthy controls, in order to provide a more comprehensive understanding of LV diastolic function. 4D Flow MRI data were acquired in ICM patients (n = 15) and healthy controls (n = 15). The λ2 method was used to extract VRs during early and late diastolic filling. Geometrical VR features, e.g., circularity index (CI), orientation (α), and inclination with respect to the LV outflow tract (ß), were extracted. Kinetic energy (KE), rate of viscous energy loss ( ĖL̇ ), vorticity (W), and volume (V) were computed for each VR; the ratios with the respective quantities computed for the entire LV were derived. At peak E-wave, the VR was less circular (p = 0.032), formed a smaller α with the LV long-axis (p = 0.003) and a greater ß (p = 0.002) in ICM patients as compared to controls. At peak A-wave, CI was significantly increased (p = 0.034), while α was significantly smaller (p = 0.016) and β was significantly increased (p = 0.036) in ICM as compared to controls. At both peak E-wave and peak A-wave, ĖL̇_VR/ĖL̇_LV , WVR/WLV, and VVR/VLV significantly decreased in ICM patients vs. healthy controls. KEVR/VVR showed a significant decrease in ICM patients with respect to controls at peak E-wave, while VVR remained comparable between normal and pathologic conditions. In the analyzed ICM patients, the diastolic VRs showed alterations in terms of geometry and energetics. These derangements might be attributed to both structural and functional alterations affecting the infarcted wall region and the remote myocardium.

Purpose: False lumen (FL) expansion often occurs in type B aortic dissection (TBAD) and has been associated with the presence of re-entry tears. This longitudinal study aims to elucidate the role of re-entry tears in the progression of TBAD using a controlled swine model, by assessing aortic hemodynamics through combined imaging and computational modeling. Materials and Methods: A TBAD swine model with a primary entry tear at 7 cm distal to the left subclavian artery was created in a previous study. In the current study, reintervention was carried out in this swine model to induce 2 additional re-entry tears of approximately 5 mm in diameter. Computed tomography (CT) and 4-dimensional (4D) flow magnetic resonance imaging (MRI) scans were taken at multiple follow-ups before and after reintervention. Changes in aortic volume were measured on CT scans, and hemodynamic parameters were evaluated based on dynamic data acquired with 4D-flow MRI and computational fluid dynamics simulations incorporating all available in vivo data. Results: Morphological analysis showed FL growth of 20% following the initial TBAD-growth stabilized after the creation of additional tears and eventually FL volume reduced by 6%. Increasing the number of re-entry tears from 1 to 2 caused flow redistribution, with the percentage of true lumen (TL) flow increasing from 56% to 78%; altered local velocities; reduced wall shear stress surrounding the tears; and led to a reduction in FL pressure and pressure difference between the 2 lumina. Conclusion: This study combined extensive in vivo imaging data with sophisticated computational methods to show that additional re-entry tears can alter dissection hemodynamics through redistribution of flow between the TL and FL. This helps to reduce FL pressure, which could potentially stabilize aortic growth and lead to reversal of FL expansion. This work provides a starting point for further study into the use of fenestration in controlling undesirable FL expansion. Clinical Impact Aortic growth and false lumen (FL) patency are associated with the presence of re-entry tears in type B aortic dissection (TBAD) patients. Guidelines on how to treat re-entry tears are lacking, especially with regards to the control and prevention of FL expansion. Through a combined imagining and computational hemodynamics study of a controlled swine model, we found that increasing the number of re-entry tears reduced FL pressure and cross lumen pressure difference, potentially stabilising aortic growth and leading to FL reduction. Our findings provide a starting point for further study into the use of fenestration in controlling undesirable FL expansion.

Background and Objective Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data. Methods 133 coronary arteries were reconstructed form CT images of patients suffering from CAD. For each vessel, invasive FFR was measured. After segmentation, the geometries were prepared for CFD simulation by clipping the outlets and discretizing into tetrahedral mesh. Steady BCs were defined in two steps: i) rest BCs were extrapolated from clinical and image-derived data; ii) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation, until patient's aortic pressure was matched. Pulsatile BCs were defined exploiting the convergence values of steady BCs. After CFD simulation, lesion-specific hemodynamic indexes were computed and compared between group of patients for which surgery was indicated and not. The whole pipeline was implemented as a straightforward process, in which each single step is performed automatically. Results Steady and pulsatile FFR-CT yielded a strong correlation (r=0.988, p<0.001) and correlated with invasive FFR (r=0.797, p<0.001). The per-point difference between the pressure and FFR-CT field predicted by the two methods was below 1% and 2%, respectively. Both approaches exhibited a good diagnostic performance: accuracy was 0.860 and 0.864, the AUC was 0.923 and 0.912, for steady and pulsatile case, respectively. The computational time required by steady BCs CFD was approximatively 30-folds lower than pulsatile case. Conclusions This work shows the feasibility of using steady BCs CFD for computing the FFR-CT in coronary arteries, as well as its computational and diagnostic performance within a fully automated pipeline.

Three-dimensional transesophageal echocardiography (3DTEE) is the recommended imaging technique for the assessment of mitral valve (MV) morphology and lesions in case of mitral regurgitation (MR) requiring surgical or transcatheter repair. Such assessment is key to thorough intervention planning and to intraprocedural guidance. However, it requires segmentation from 3DTEE images, which is timeconsuming, operator-dependent, and often merely qualitative. In the present work, a novel workflow to quantify the patient-specific MV geometry from 3DTEE is proposed. The developed approach relies on a 3D multi-decoder residual convolutional neural network (CNN) with a U-Net architecture for multi-class segmentation of MV annulus and leaflets. The CNN was trained and tested on a dataset comprising 55 3DTEE examinations of MR-affected patients. After training, the CNN is embedded into a fully automatic, and hence fully repeatable, pipeline that refines the predicted segmentation, detects MV anatomical landmarks and quantifies MV morphology. The trained 3D CNN achieves an average Dice score of $0.82 \pm 0.06$, mean surface distance of $0.43 \pm 0.14$ mm and 95% Hausdorff Distance (HD) of $3.57 \pm 1.56$ mm before segmentation refinement, outperforming a state-of-the-art baseline residual U-Net architecture, and provides an unprecedented multi-class segmentation of the annulus, anterior and posterior leaflet. The automatic 3D linear morphological measurements of the annulus and leaflets, specifically diameters and lengths, exhibit differences of less than 1.45 mm when compared to ground truth values. These measurements also demonstrate strong overall agreement with analyses conducted by semi-automated commercial software. The whole process requires minimal user interaction and requires approximately 15 seconds

Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis. Our method exploits supervised learning for the segmentation of the lumen, and then, CAC and CorT are automatically quantified. 281 manually annotated CCTA images were used to train a two-stage U-Net-based architecture. The first stage employed a 2.5D U-Net trained on axial, coronal, and sagittal slices for preliminary segmentation, while the second stage utilized a multichannel 3D U-Net for refinement. Then, a geometric post-processing was implemented: vessel centerlines were extracted, and tortuosity score was quantified as the count of branches with three or more bends with change in direction forming an angle >45°. CAC scoring relied on image attenuation. CAC was detected by setting a patient specific threshold, then a region growing algorithm was applied for refinement. The application of the complete pipeline required <5 min per patient. The model trained for coronary segmentation yielded a Dice score of 0.896 and a mean surface distance of 1.027 mm compared to the reference ground truth. Tracts that presented stenosis were correctly segmented. The vessel tortuosity significantly increased locally, moving from proximal, to distal regions (p < 0.001). Calcium volume score exhibited an opposite trend (p < 0.001), with larger plaques in the proximal regions. Volume score was lower in patients with a higher tortuosity score (p < 0.001). Our results suggest a linked negative correlation between tortuosity and calcific plaque formation. We implemented a fast and objective tool, suitable for population studies, that can help clinician in the quantification of CAC and various coronary morphological parameters, which is helpful for CAD risk assessment.

BACKGROUND AND OBJECTIVE:4D flow magnetic resonance imaging provides time-resolved blood flow velocity measurements, but suffers from limitations in spatio-temporal resolution and noise. In this study, we investigated the use of sinusoidal representation networks (SIRENs) to improve denoising and super-resolution of velocity fields measured by 4D flow MRI in the thoracic aorta.METHODS:Efficient training of SIRENs in 4D was achieved by sampling voxel coordinates and enforcing the no-slip condition at the vessel wall. A set of synthetic measurements were generated from computational fluid dynamics simulations, reproducing different noise levels. The influence of SIREN architecture was systematically investigated, and the performance of our method was compared to existing approaches for 4D flow denoising and super-resolution.RESULTS:Compared to existing techniques, a SIREN with 300 neurons per layer and 20 layers achieved lower errors (up to 50% lower vector normalized root mean square error, 42% lower magnitude normalized root mean square error, and 15% lower direction error) in velocity and wall shear stress fields. Applied to real 4D flow velocity measurements in a patient-specific aortic aneurysm, our method produced denoised and super-resolved velocity fields while maintaining accurate macroscopic flow measurements.CONCLUSIONS:This study demonstrates the feasibility of using SIRENs for complex blood flow velocity representation from clinical 4D flow, with quick execution and straightforward implementation.

Introduction Transcatheter aortic valve implantation (TAVI) has become an alternative to surgical replacement of the aortic valve elderly patients. However, TAVI patients may suffer from paravalvular leaks (PVL). Detecting and grading is usually done by echocardiography, but is limited by resolution, 2D visualization and operator dependency. 4D flow magnetic resonance imaging (MRI) is a promising alternative, which did not reach clinical application in TAVI patients. The aim of this study was applying 3D printing technologies in order to evaluate flow patterns and hemodynamics of PVLs following TAVI, exploiting 4D flow MRI and standard ultrasound. Materials and methods An MR-compatible, anatomically left ventricle, aortic root, and ascending aorta model was fabricated by combining 3D-printed parts and various soft silicone materials to match physiological characteristics. An Abbott Portico™ valve was used in continuous antegrade flow (12–22 l/min), retrograde flow with varying transvalvular pressures (60–110 mmHg), and physiological pulsatile hemodynamics (aortic pressure: 120/80 mmHg, cardiac output: 5 l/min) Time-resolved MR measurements were performed above and below the TAVI stent and compared with color Doppler ultrasound measurements in exactly the same setup. Results The continuous antegrade flow measurements from MRI largely agreed with the flowmeter measurements, and a maximum error of only 7% was observed. In the retrograde configuration, visualization of the paravalvular leaks was possible from the MR measurements, but flow was overestimated by up to 33%. The 4D MRI measurement in the pulsatile setup revealed a single main PVL, which was also confirmed by the color Doppler measurements, and velocities were similar (2.0 m/s vs. 1.7 m/s). Discussion 4D MRI techniques were used to qualitatively assess flow in a patient-specific, MR-compatible and flexible model, which only became possible through the use of 3D printing techniques. Flow patterns in the ascending aorta, identification and quantification of PVLs was possible and the location and extent of PVLs were confirmed by ultrasound measurements. The 4D MRI flow technique allowed evaluation of flow patterns in the ascending aorta and the left ventricle below the TAVI stent with good results in identifying PVLs, demonstrating its capabilities over ultrasound by providing the ability to visualize the paravalvular jets in three dimensions at however, additional expenditure of time and money.

BACKGROUND AND OBJECTIVE:Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA.METHODS:Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated.RESULTS:Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors.CONCLUSIONS:We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.

Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): This work was supported by IRCCS Policlinico San Donato, a Clinical Research Hospital partially funded by the Italian Ministry of Health. Introduction Blood flowing into the left ventricle (LV) forms a 3D vortex ring starting from the free margin of mitral valve leaflets and encompassing the inflow jet. Vortex ring formation is presumed to store part of the kinetic energy of the entering jet and help directing blood flow towards the aorta [1]. Changes in vortex formation are associated to altered endocardial wall motion and LV chamber remodeling, as in case of ischemic cardiomyopathy (ICM). 4D Flow MRI is currently the unique volumetric imaging technique to assess in vivo changes of diastolic vortex ring. Purpose To characterize changes in LV diastolic vortex ring and associated energetics in a cohort of ICM patients (n = 12) vs. controls (n = 12) with comparable age. Methods 4D Flow MRI data were acquired for ICM patients presenting with anterior myocardial infarction and in healthy controls. The diastolic vortex ring was assessed at peak E-wave and at peak A-wave through the λ2 method [2]. Geometrical features of the vortex ring, e.g., circularity index (CI) and vortex orientation (α), were extracted (Figure 1). Kinetic energy (KE), rate of viscous energy loss (EL), vorticity and volume were computed for each vortex ring core; the ratios with the respective quantities computed for the entire LV were derived. Results At peak E-wave, the vortex ring was less circular (p = 0.017) and formed a smaller α with the mitral valve plane (p = 0.024) in ICM patients as compared to controls. At peak A-wave, the vortex ring core remained less circular (p = 0.027) in ICM patients, while vortex orientation remained comparable between the two groups. KE ratio, EL ratio, vorticity ratio and volume ratio significantly decreased for ICM patients at both E-peak and A-peak (Table 1). Conclusions Though ICM is primarily addressed and investigated as a systolic dysfunction, diastolic function can be deranged too, as highlighted by altered diastolic vortex ring shape and energetics.

Accurate planning of transcatheter aortic implantation (TAVI) is important to minimize complications, and it requires anatomic evaluation of the aortic root (AR), commonly done through 3D computed tomography (CT) image analysis. Currently, there is no standard automated solution for this process. Two convolutional neural networks (CNNs) with 3D U-Net architectures (model 1 and model 2) were trained on 310 CT scans for AR analysis. Model 1 performed AR segmentation and model 2 identified the aortic annulus and sinotubular junction (STJ) contours. Results were validated against manual measurements of 178 TAVI candidates. After training, the two models were integrated into a fully automated pipeline for geometric analysis of the AR. The trained CNNs effectively segmented the AR, annulus and STJ, resulting in mean Dice scores of 0.93 for the AR, and mean surface distances of 1.16 mm and 1.30 mm for the annulus and STJ, respectively. Automatic measurements were in good agreement with manual annotations, yielding annulus diameters that differed by 0.52 [-2.96, 4.00] mm (bias and 95% limits of agreement for manual minus algorithm). Evaluating the area-derived diameter, bias and limits of agreement were 0.07 [-0.25, 0.39] mm. STJ and sinuses diameters computed by the automatic method yielded differences of 0.16 [-2.03, 2.34] and 0.1 [-2.93, 3.13] mm, respectively. The proposed tool is a fully automatic solution to quantify morphological biomarkers for pre-TAVI planning. The method was validated against manual annotation from clinical experts and showed to be quick and effective in assessing AR anatomy, with potential for time and cost savings.

Riva A, Sturla F, Pica S, Camporeale A, Tondi L, Saitta S, Caimi A, Giese D, Palladini G, Milani P, Castelvecchio S, Menicanti L, Redaelli A, Lombardi M, Votta E. Comparison of Four-Dimensional Magnetic Resonance Imaging Analysis of Left Ventricular Fluid Dynamics and Energetics in Ischemic and Restrictive Cardiomyopathies. J Magn Reson Imaging. 2022 Oct;56(4):1157-1170. doi: 10.1002/jmri.28076. Epub 2022 Jan 24. PMID: 35075711; PMCID: PMC9541919. Abstract Background: Time-resolved three-directional velocity-encoded (4D flow) magnetic resonance imaging (MRI) enables the quantification of left ventricular (LV) intracavitary fluid dynamics and energetics, providing mechanistic insight into LV dysfunctions. Before becoming a support to diagnosis and patient stratification, this analysis should prove capable of discriminating between clearly different LV derangements. Purpose: To investigate the potential of 4D flow in identifying fluid dynamic and energetics derangements in ischemic and restrictive LV cardiomyopathies. Study type: Prospective observational study. Population: Ten patients with post-ischemic cardiomyopathy (ICM), 10 patients with cardiac light-chain cardiac amyloidosis (AL-CA), and 10 healthy controls were included. Field strength/sequence: 1.5 T/balanced steady-state free precession cine and 4D flow sequences. Assessment: Flow was divided into four components: direct flow (DF), retained inflow, delayed ejection flow, and residual volume (RV). Demographics, LV morphology, flow components, global and regional energetics (volume-normalized kinetic energy [KEV ] and viscous energy loss [ELV ]), and pressure-derived hemodynamic force (HDF) were compared between the three groups. Statistical tests: Intergroup differences in flow components were tested by one-way analysis of variance (ANOVA); differences in energetic variables and peak HDF were tested by two-way ANOVA. A P-value of <0.05 was considered significant. Results: ICM patients exhibited the following statistically significant alterations vs. controls: reduced KEV , mostly in the basal region, in systole (-44%) and in diastole (-37%); altered flow components, with reduced DF (-33%) and increased RV (+26%); and reduced basal-apical HDF component on average by 63% at peak systole. AL-CA patients exhibited the following alterations vs. controls: significantly reduced KEV at the E-wave peak in the basal segment (-34%); albeit nonstatistically significant, increased peaks and altered time-course of the HDF basal-apical component in diastole and slightly reduced HDF components in systole. Data conclusion: The analysis of multiple 4D flow-derived parameters highlighted fluid dynamic alterations associated with systolic and diastolic dysfunctions in ICM and AL-CA patients, respectively.

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation ( p = 0.015) and tortuosity ( p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters ( p < 0.0001), angulation ( p < 0.0001), and tortuosity ( p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.